DeepLigType: Predicting Ligand Types of Protein-Ligand Binding Sites Using a Deep Learning Model.

The analysis of protein-ligand binding sites plays a crucial role in the initial stages of drug discovery. Accurately predicting the ligand types that are likely to bind to protein-ligand binding sites enables more informed decision making in drug design. Our study, DeepLigType, determines protein-ligand binding sites using Fpocket and then predicts the ligand type of these pockets with the deep learning model, Convolutional Block Attention Module (CBAM) with ResNet.

Value-based modeling for mobile health application development.

Background: In this paper is presented the use of value-based modeling, traditionally a business development tool, for the improvement of mobile health app design. The conceptual foundations for this work are design science, which is the scientific study and creation of artifacts, and convergence, which is a research method that in this case combines engineering with medicine. Relevant previous work done by the research team included the modeling of a case management system using process-based and information-based modeling techniques.

The emerging postural instability phenotype in idiopathic Parkinson disease.

Identification of individuals at high risk for rapid progression of motor and cognitive signs in Parkinson disease (PD) is clinically significant. Postural instability and gait dysfunction (PIGD) are associated with greater motor and cognitive deterioration. We examined the relationship between baseline clinical factors and the development of postural instability using 5-year longitudinal de-novo idiopathic data (n = 301) from the Parkinson's Progressive Markers Initiative (PPMI).